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The AAPG/Datapages Combined Publications Database
Showing 23,348 Results. Searched 200,626 documents.
Convolution neural networks fault interpretation in the Brazilian presalt
Hugo Garcia, Edimar Perico, Ana Moliterno, Alexandre Kolisnyk, Michael Lowsby
International Meeting for Applied Geoscience and Energy (IMAGE)
..., particularly deep learning convolutional neural networks have been used successfully in fault interpretation in seismic data around the world with different...
2024
An Introduction to Deep Learning: Part II
Lasse Amundsen, Hongbo Zhou, Martin Landrø
GEO ExPro Magazine
... often the model fails to predict the correct answer in their top five guesses (the top-5 error rate), in descending order of confidence. ILSVRC 2012...
2017
Abstract: Kirchhoff Imaging with Adaptive Greens Functions for Compensation for Dispersion, Attenuation, and Velocity Imprecision; #90187 (2014)
Andrew V. Barrett
Search and Discovery.com
... the imaging at higher frequencies. Here I present a method for deriving and applying adaptively a short, white operator to compensate...
2014
Deterministic and Statistical Wavelet Processing
Lee Lu
Southeast Asia Petroleum Exploration Society (SEAPEX)
... on the convolutional model for a seismic trace: it is assumed that an observed trace, x, is the convolution of an “effective wavelet”, w, with an “effective reflectivity...
1980
Accurate seismic data interpolation based on multiband intelligent training
Xueyi Sun, Benfeng Wang, Tongtong Mo
International Meeting for Applied Geoscience and Energy (IMAGE)
... information about subsurface structures and geological features. During the optimization of convolutional neural network (CNN)-assisted seismic data...
2023
Deep Learning Models for Methane Emissions Identification and Quantification
Ismot Jahan, Mohamed Mehana, Bulbul Ahmmed, Javier E. Santos, Dan O’Malley, Hari Viswanathan
Unconventional Resources Technology Conference (URTEC)
... to prepare the data for the machine learning model. In this section, we will outline the preprocessing and Convolutional Neural Network (CNN) model...
2023
3D velocity model building based upon hybrid neural network
Herurisa Rusmanugroho, Junxiao Li, M. Daniel Davis Muhammed, Jian Sun
International Meeting for Applied Geoscience and Energy (IMAGE)
... as an image are passed through some convolutional layers to estimate P-velocity model. This network is expected to learn from the features obtained from...
2022
Seismic impedance inversion via neural networks and linear optimization algorithm
Bo Zhang, Yitao Pu, Ruiqi Dai, Danping Cao
International Meeting for Applied Geoscience and Energy (IMAGE)
..., and a low frequency model. The loss function of PINNs is designed to minimize the difference between real seismograms and synthetic seismic...
2024
Deep convolutional neural networks for generating grain-size logs from core photographs
Thomas T. Tran, Tobias H. D. Payenberg, Feng X. Jian, Scott Cole, and Ishtar Barranco
AAPG Bulletin
...Deep convolutional neural networks for generating grain-size logs from core photographs Thomas T. Tran, Tobias H. D. Payenberg, Feng X. Jian, Scott...
2022
Noise suppression and compressive sensing recovery with seismic-adapted DnCNN within RED
Nasser Kazemi
International Meeting for Applied Geoscience and Energy (IMAGE)
... is an additive white Gaussian noise. In this model, DnCNN acts as a noise-estimating operator L (m) ⇡ n, and s ⇡ m L (m), (5) where L (·) is the DnCNN...
2024
Seismic Forward Modeling of Semberah Fluvio-Deltaic Reservoir
Adi Widyantoro, Wahyu Dwijo Santoso
Indonesian Petroleum Association
... modeling at each UKM wells to understand lithology and fluid effects over amplitude variations, 3) conceptual 2D convolutional model to understand boundary...
2021
Machine learning applications to seismic structural interpretation: Philosophy, progress, pitfalls, and potential
Kellen L. Gunderson, Zhao Zhang, Barton Payne, Shuxing Cheng, Ziyu Jiang, and Atlas Wang
AAPG Bulletin
... amplitude (grayscale) and fault probability from convolutional neural network (CNN) (red-white scale). The CNN model accurately predicts the steeply dipping...
2022
Abstract: Recovering Low Frequencies for Impedance Inversion by Frequency Domain Deconvolution; #90224 (2015)
Sina Esmaeili and Gary Frank
Search and Discovery.com
... reflectivity. We start by reintroducing the convolutional model for normal incident seismograms and then show how reflectivity can be estimated...
2015
Post Migration Processing of Seismic Data
Dashuki Mohd.
Geological Society of Malaysia (GSM)
... or multiples. The basis for deconvolution is the convolutional model (Robinson, 1984). In the convolutional model, a seismic trace is viewed...
1994
Deep learning to predict subsurface properties from injected CO2 plume bodies using time-lapse seismic shot gathers
Son Phan, Wenyi Hu, Aria Abubakar
International Meeting for Applied Geoscience and Energy (IMAGE)
... without conventional velocity model building and imaging. A deep learning architecture with a new multi-branch design with different filtering sizes...
2022
Methods of estimating wavelet stationarity, stabilizing non-stationarity, and evaluating its impact on inversion: A synthetic example using SEAM II Barrett unconventional model
Jesse Buckner, Michael Fry, Joe Zuech, Peter Harris, Bill Shea
International Meeting for Applied Geoscience and Energy (IMAGE)
... is simulated across a continuous 3D convolutional synthetic seismic volume, derived from the earth model of the SEAM II Barrett dataset. Multiple...
2023
Innovative disorder seismic attribute for reservoir characterization
Qiang Fu, Saleh Al-Dossary
International Meeting for Applied Geoscience and Energy (IMAGE)
... seismic attribute is a convolutional filtering based algorithm designed using an optimization approach. By design, the attribute is insensitive to faults...
2022
VSP Guided Reprocessing and Inversion of Surface Seismic Data
R. Gir, Dominique Pajot, Serge Des Ligneris
Southeast Asia Petroleum Exploration Society (SEAPEX)
... seismic data is known as the “convolutional model of the seismogram”. This model states that after proper data processing, the final seismic data has...
1988
Embedding Physical Flow Functions into Deep Learning Predictive Models for Improved Production Forecasting
Syamil Mohd Razak, Jodel Cornelio, Young Cho, Hui-Hai Liu, Ravimadhav Vaidya, Behnam Jafarpour
Unconventional Resources Technology Conference (URTEC)
...trained model is composed of several fully-connected regression layers and one- URTeC 3702606 6 dimensional (1D) convolutional layers. A fully-co...
2022
4D Finite Difference Forward Modeling within a Redefined Closed-Loop Seismic Reservoir Monitoring Workflow, #41922 (2016).
David Hill, Dominic Lowden, Sonika, Chris Koeninger
Search and Discovery.com
...-field coupled dynamic integrated earth model to surface. From which 3D grids of petro-elastic parameters for a range of reservoir simulations...
2016
Seismic reflectivity inversion via a regularized deep image prior
Hongling Chen, Mauricio D. Sacchi, Jinghuai Gao
International Meeting for Applied Geoscience and Energy (IMAGE)
... assist in characterizing the subsurface. By adopting the stationary convolution model, seismic reflectivity inversion is posed as a multichannel deblurring...
2022
Noise analysis and ML denoising of DAS VSP data acquired from ESP lifted wells
Ge Zhan, Yao Zhao, Cheng Cheng, Josef Heim, Weihong Fei, Mike Craven, Scott Baker, Gilles Hennenfent
International Meeting for Applied Geoscience and Energy (IMAGE)
... developed a machine learning (ML) workflow that uses a deep convolutional U-Net architecture to model the ESP noise first and then subtract it from...
2022
Machine-learning Facilitates Prediction of Geomechanical Properties Directly From SEM Images in Unconventional Plays
Heehwan Yang, Deepak Devegowda, Mark Curtis, Chandra Rai
Unconventional Resources Technology Conference (URTEC)
... non-parametric regression resulting in a unified, easily generalizable model that performs robustly when tested against previously unseen images. Our...
2023
GeoStreamer X Delivers Near-Field Multi-Azimuth Dataset for Accurate Lead Characterisation, South Viking Graben, Norway
Cyrille Reiser, Eric Mueller, PGS
GEO ExPro Magazine
... summarised below: • Comprehensive demultiple sequence addressing the short and long period multiples integrating 3D convolutional and wave equation...
2021
Introduction to Deep Learning: Part I
Hongbo Zhou, Lasse Amundsen, Martin Landrø
GEO ExPro Magazine
... of some objective or loss function on a training set of examples. Loss functions express the misfit between the predictions of the model being...
2017